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Record W2772681431 · doi:10.1002/ejsp.2355

Cultural identity dynamics: Capturing changes in cultural identities over time and their intraindividual organization

2017· article· en· W2772681431 on OpenAlexafffund
Catherine E. Amiot, Marina M. Doucerain, Biru Zhou, Andrew G. Ryder

Bibliographic record

VenueEuropean Journal of Social Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia UniversityMcGill UniversityUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAcculturationSocial psychologyIdentity (music)Longitudinal studySocial identity theoryCategorizationCultural identityCompartmentalization (fire protection)Identity changeBaseline (sea)Ethnic groupDevelopmental psychologySocial groupSociologyPolitical scienceAnthropologyEpistemology

Abstract

fetched live from OpenAlex

Abstract Important life transitions – such as migration – have the potential to enrich one's sense of self, but they are also demanding and challenging. The current research investigates how cultural identities change and become configured over time among newly arrived international students and the social factors that predict these longitudinal changes. A four‐wave longitudinal study was conducted during international students' first year in their new country ( N = 278). Multivariate hierarchical linear modeling analyses allowed us to unpack both baseline (between‐person) and intraindividual change (within‐person) effects. Whereas increased psychological need satisfaction via both the new and one's heritage cultural group predicted increased identity integration, greater discrimination (i.e., both at baseline and an increase over time) predicted increased compartmentalization and the predominance (categorization) of one identity over the others. Results are discussed in light of novel theoretical developments in the acculturation and identity change literatures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.380
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2017
Admission routes2
Has abstractyes

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